DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1-8-2024 is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 is directed to a processor identifying and determining. These steps express a mathematical algorithm, which have been determined by the courts to be abstract ideas. E.g. in Digitech Image Techs., LLC v. Elecs. for Imaging, Inc. the court determined “Without additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claims 2-15 further detail the earlier algorithm steps and generic computers. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Examiner’s Note: For applicant’s benefit portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS. See MPEP 2141.02 VI.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jahromi, et. al., “Real Time Hybrid Multi Sensor Fusion Framework for Perception in Autonomous Vehicles”, published October 2019.
As per claim 1, Jahromi discloses a computer implemented method for determining a property of an object, the method comprising the following steps carried out by computer hardware components:
acquiring an image of a scene comprising the object (Jahromi, section 3.1); acquiring a plurality of lidar measurements of the scene; clustering the plurality of lidar measurements into a plurality of groups of lidar measurements (Jahromi, section 3.3);
acquiring a radar measurement of the scene (Jahromi, section 3.2);
identifying which of the plurality of groups of lidar measurements corresponds to the radar measurement (Jahromi, section 4);
determining the property of the object based on the image and the identified group of lidar measurements (Jahromi, section 4.1).
As per claim 2, Jahromi further discloses the computer implemented method of claim 1, further comprising the following step carried out by the computer hardware components: determining whether at least one group of lidar measurements of the plurality of groups of lidar measurements corresponds to the radar measurement (Jahromi, section 4).
As per claim 3, Jahromi further discloses the computer implemented method of claim 1, wherein the radar measurement and/ or the plurality of lidar measurements corresponds to an area in the scene which comprises the object (Jahromi, section 4).
As per claims 4-8, Jahromi further discloses the computer implemented method of claim 1, further comprising the following steps carried out by the computer hardware components: determining a respective first distance hypothesis of each of the group of lidar measurements based on the lidar measurements in the respective group of lidar measurements; and determining a second distance hypothesis based on the radar measurement; wherein it is identified which of the plurality of groups of lidar measurements corresponds to the radar measurement based on a correspondence between the respective first distance hypothesis and the second distance hypothesis (Jahromi, section 4.2.3 fusing radar and lidar data based on characteristics such as location and speed which is measured over time).
As per claim 9, Jahromi further discloses the computer implemented method of claim 1, further comprising the following step carried out by the computer hardware components: carrying out object detection and/ or classification based on the acquired image (Jahromi, section 4.1.1).
As per claim 10, Jahromi further discloses the computer implemented method of claim 1:wherein the property comprises at least one of: a class of the object, a position of the object, a distance of the object, and a velocity of the object (Jahromi, Fig. 16).
As per claim 11, Jahromi further discloses a training method for training a machine learning method, the training method comprising the following steps carried out by computer hardware components: determining ground truth data based on the computer implemented method for determining a property of an object of claim 1; and training the machine learning method based on the ground truth data (Jahromi, section 4.1-4.1.1, road segmentation).
As per claim 12, Jahromi further discloses a control method for controlling a vehicle, the control method comprising the following steps carried out by computer hardware components: determining information related to a surrounding of the vehicle based on the computer implemented method for determining a property of an object of claim 1; and controlling the vehicle based on the information (Jahromi, section 6).
As per claim 13, Jahromi further discloses a computer system comprising a plurality of computer hardware components configured to carry out the steps of the computer implemented method of claim 1 (Jahromi, Fig. 3).
As per claim 14, Jahromi further discloses a vehicle, comprising: the computer system of claim 13; a camera configured to acquire the image; a lidar sensor configured to acquire the plurality of lidar measurements; and a radar sensor configured to acquire the radar measurements (Jahromi, Fig. 1).
As per claim 15, Jahromi further discloses a non-transitory computer readable medium comprising instructions for carrying out the computer implemented method of claim 1 (Jahromi, section 5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is provided on form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCUS E WINDRICH whose telephone number is (571)272-6417. The examiner can normally be reached M-F ~7-3:30.
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/MARCUS E WINDRICH/ Primary Examiner, Art Unit 3646